Data Science for Mental Health (DS4MH) @ The Alan Turing Institute
About Us
The vision for this interest group is to kick-start one or more projects using contemporary data science and multi-modal data for mental health to provide insight and benefit for individuals, clinicians, and contribute to fundamental research in mental health (including dementia) as well as the data science methodology. It aims to provide an informal bridge between clinicians, charities, and data owners (like CRIS, UKDP, and Biobank) and data science researchers to stimulate and align cutting edge research in this area.
Events
Meetings
We organise monthly meetings (including half-an-hour long invited talks) at the Turing. Meetings are organised and moderated by Jenny Chim, Yue Wu, and Emilio Ferrucci. Please join our mailing list for more updated information.
As a part of AI UK Fringe, we jointly organised a hybrid event with the NLP interest group on AI for Mental Health Monitoring on 28th March 2024.
See here for our previous talks.
Upcoming Events
Meetings
| Date | Time | Presenter | Title |
|---|---|---|---|
| 2025.02.20 | 15:00 | Introduction | |
| 15:05 | Yehu Chen (Washington University in St. Louis) |
Personalized Personality Modeling with Gaussian Processes: Insights from a New Longitudinal Dataset
The Big Five Personality framework has been widely used in social science to study socioeconomic and political behavior. However, an ongoing debate in psychology questions whether personality research should prioritize universal principles (nomothetic) or focus on individual-specific patterns (idiographic). To address this gap, we collect the first large-scale longitudinal personality dataset of its kind and introduce the Idiographic Personality Gaussian Process (IPGP) framework for modeling dynamic psychological traits. We first validate IPGP through factor analysis on standard cross-sectional datasets, and then apply IPGP to uncover individualized personality patterns on our longitudinal dataset. We also demonstrate how external environments influence internal personality traits using our framework, offering new insights into personalized psychological assessment. |
|
| 15:40 | Dr. Dong Whi Yoo (Kent State University) |
Lived Experience Centered AI in Mental Health
Artificial intelligence (AI) holds transformative potential in mental health, offering solutions to critical challenges such as resource scarcity and lack of awareness. However, the rapid advancements and hype surrounding AI often marginalize the voices of those most impacted—people with lived experiences of mental health concerns. This talk examines how individuals with lived experience perceive mental health AI technologies and explores how to design solutions centered around their values and needs. First, I studied a federally funded mental health AI research project, examining how individuals with lived experiences were treated primarily as data contributors rather than as research partners. Building on these insights, I explore how meaningful collaborations with these individuals can inform the design of a schizophrenia relapse management tool. I also investigate the potential of large language models (LLMs) in supporting self-management of depression, focusing on the unique perspectives and priorities of those with lived experience. By centering these voices, this work highlights the need for inclusive innovation and reimagines the role of AI in the future of mental health. |
|
| 16:20 | After talks discussion |